Forecast of the Stock Market Price using Artificial Neural Network and Wavelet Transform

Hyunsu Ha, Kyungmo Ha

http://doi.org/10.5626/JOK.2019.46.12.1249

With advancements in technologies on machine learning and artificial neural network, various researches have attempted to predict the changes in the price of the stock market. The prediction accuracy has improved with adoption of new artificial neural network technologies that have been developed for image and voice signal processing. In the present work, the technical indices from KOSPI were decomposed for the prediction of index and movement direction of KOSPI into high-frequency part and low-frequency part using wavelet transform, then used to predict KOSPI independently by using artificial neural networks. For the final prediction, the prediction result of each frequency part was added. CNN, DPN, and LSTM were employed as artificial neural network; the performance of each model was compared and the efficiency of the wavelet transform of input variables was analyzed. CNN with 0.51% of MAPE for the index prediction and LSTM with 81.7% of accuracy for movement prediction showed the best performance among the three models. The efficiency of wavelet transform was confirmed with averaged 38% of the improved performance for the index prediction and averaged 25% of the improved performance for the movement prediction.